Zobrazeno 1 - 10
of 4 960
pro vyhledávání: '"ZHANG, HONGBO"'
Autor:
He, Jing, Li, Haodong, Yin, Wei, Liang, Yixun, Li, Leheng, Zhou, Kaiqiang, Zhang, Hongbo, Liu, Bingbing, Chen, Ying-Cong
Leveraging the visual priors of pre-trained text-to-image diffusion models offers a promising solution to enhance zero-shot generalization in dense prediction tasks. However, existing methods often uncritically use the original diffusion formulation,
Externí odkaz:
http://arxiv.org/abs/2409.18124
Autor:
Shi, Lei, Liu, Zhimeng, Yang, Yi, Wu, Weize, Zhang, Yuyang, Zhang, Hongbo, Lin, Jing, Wu, Siyu, Chen, Zihan, Li, Ruiming, Wang, Nan, Liu, Zipeng, Tan, Huobin, Gao, Hongyi, Zhang, Yue, Wang, Ge
The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides d
Externí odkaz:
http://arxiv.org/abs/2408.04665
Autor:
Li, Jianhao, Sun, Tianyu, Wang, Zhongdao, Xie, Enze, Feng, Bailan, Zhang, Hongbo, Yuan, Ze, Xu, Ke, Liu, Jiaheng, Luo, Ping
This paper proposes an algorithm for automatically labeling 3D objects from 2D point or box prompts, especially focusing on applications in autonomous driving. Unlike previous arts, our auto-labeler predicts 3D shapes instead of bounding boxes and do
Externí odkaz:
http://arxiv.org/abs/2407.11382
Model Predictive Control (MPC) relies heavily on the robot model for its control law. However, a gap always exists between the reduced-order control model with uncertainties and the real robot, which degrades its performance. To address this issue, w
Externí odkaz:
http://arxiv.org/abs/2407.10124
Autor:
Yue, Linzhu, Zhang, Lingwei, Song, Zhitao, Zhang, Hongbo, Dong, Jinhu, Zeng, Xuanqi, Liu, Yun-Hui
Exploring the limits of quadruped robot agility, particularly in the context of rapid and real-time planning and execution of omnidirectional jump trajectories, presents significant challenges due to the complex dynamics involved, especially when con
Externí odkaz:
http://arxiv.org/abs/2407.00658
Diffusion Models (DMs) utilize an iterative denoising process to transform random noise into synthetic data. Initally proposed with a UNet structure, DMs excel at producing images that are virtually indistinguishable with or without conditioned text
Externí odkaz:
http://arxiv.org/abs/2406.11100
Autor:
Yang, Hao, Zhao, Yanyan, Wu, Yang, Wang, Shilong, Zheng, Tian, Zhang, Hongbo, Ma, Zongyang, Che, Wanxiang, Qin, Bing
Compared to traditional sentiment analysis, which only considers text, multimodal sentiment analysis needs to consider emotional signals from multimodal sources simultaneously and is therefore more consistent with the way how humans process sentiment
Externí odkaz:
http://arxiv.org/abs/2406.08068
Autor:
Wang, Yidong, Guo, Qi, Yao, Wenjin, Zhang, Hongbo, Zhang, Xin, Wu, Zhen, Zhang, Meishan, Dai, Xinyu, Zhang, Min, Wen, Qingsong, Ye, Wei, Zhang, Shikun, Zhang, Yue
This paper introduces AutoSurvey, a speedy and well-organized methodology for automating the creation of comprehensive literature surveys in rapidly evolving fields like artificial intelligence. Traditional survey paper creation faces challenges due
Externí odkaz:
http://arxiv.org/abs/2406.10252
Organic weed control is a vital to improve crop yield with a sustainable approach. In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed. The robot uses a novel distributed array robot (DAR)
Externí odkaz:
http://arxiv.org/abs/2405.21056
Publikováno v:
IEEE Access 2024
This systematic literature review paper explores the use of extended reality {(XR)} technology for smart built environments and particularly for smart lighting systems design. Smart lighting is a novel concept that has emerged over a decade now and i
Externí odkaz:
http://arxiv.org/abs/2405.06928